Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A marketing team wants to launch a generative AI campaign-copy tool. Executives ask the team to justify the investment by identifying the primary business objective before any technical design begins. Which statement best represents a valid business objective for this initiative?
⚠ Common exam trap
Many exam-takers confuse a technical choice such as model size, deployment platform, or prompting method with a business objective that describes a measurable outcome.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the average time to produce approved campaign copy by a target percentage.
A valid business objective states a measurable improvement the organization wants, such as cutting the time to produce approved campaign copy. That kind of target can be baselined, tracked, and tied to value, giving executives a clear basis for funding decisions. Model selection, deployment platform, and prompting techniques are implementation choices that should be driven by the objective, not used in place of one.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use prompt engineering techniques such as few-shot examples in every request.
Why it's wrong here
Prompt engineering with few-shot examples is a technique that may improve output, but it is not a business objective. It cannot be presented to executives as the reason to fund the campaign-copy tool because it does not express a measurable change in marketing performance. Treating a prompting method as the goal would skip the step of defining what success means for the team and how it will be measured.
- ✗
Adopt the largest available foundation model for text generation.
Why it's wrong here
Choosing the largest foundation model is a technical decision, not a business objective. It does not state what the marketing team wants to improve, for whom, or by how much. In this scenario, executives are asking for justification of business value before design, so selecting a model size preempts the analysis and may increase cost without evidence that it improves copy quality or speed for the specific campaign use case.
- ✓
Reduce the average time to produce approved campaign copy by a target percentage.
Why this is correct
Reducing the time to produce approved campaign copy is a measurable business outcome that connects the generative AI tool to marketing productivity. It can be baselined before launch, tracked after rollout, and tied to labor cost or campaign velocity. Because it describes a desired operational result rather than a technology choice, it gives the team a clear target and a way to evaluate whether the investment delivered value.
- ✗
Deploy the tool on Google Kubernetes Engine with autoscaling enabled.
Why it's wrong here
Deploying on Google Kubernetes Engine with autoscaling describes an infrastructure pattern, not a business outcome. It says nothing about campaign copy quality, production speed, or cost savings. Executives seeking justification for investment need a result the business cares about; an orchestration choice is an implementation detail that should follow from requirements, not serve as the stated objective for the generative AI initiative.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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